Estimating growth patterns and driver effects in tumor evolution from individual samples

Abstract

Evolving tumors accumulate thousands of mutations. Technological advances have enabled whole genome sequencing of these mutations in large cohorts, such as those from the Pancancer Analysis of Whole Genomes (PCAWG) Consortium. The resulting data explosion has led to many methods for detecting cancer drivers through mutational recurrence and deviation from background mutation rates. However, these methods require a large cohort and underperform when recurrence is low. An alternate approach involves harnessing the variant allele frequency (VAF) of mutations in the population of tumor cells in a single individual. Moreover, ultra-deep sequencing of tumors, which is now possible, allows for particularly accurate VAF measurements, and recent studies have begun to use these to determine evolutionary trajectories and quantify subclonal selection. Here, we developed a method that quantifies tumor growth and driver effects for individual samples based solely on the VAF spectrum. Drivers introduce a perturbation into this spectrum, and our method uses the frequency of \"hitchhiking\" mutations preceding a driver to measure this perturbation. Specifically, our method applies various growth models to identify periods of positive/negative growth, the genomic regions associated with them, and the presence and effect of putative drivers. To validate our method, we first used simulation models to successfully approximate the timing and size of a drivers effect. Then, we tested our method on 993 linear tumors (i.e. those with linear subclonal expansion, where each parent-subclone has one child) from the PCAWG Consortium and found that the identified periods of positive growth are associated with drivers previously highlighted via recurrence by the PCAWG consortium. Finally, we applied our method to an ultra-deep sequenced AML tumor and identified known cancer genes and additional driver candidates. In summary, our method presents opportunities for personalized diagnosis using deep sequenced whole genome data from an individual.